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61 lines
2.1 KiB
Python
61 lines
2.1 KiB
Python
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import torch.nn as nn
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from scaling import ScaledLinear
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class Joiner(torch.nn.Module):
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def __init__(self, joiner_dim: int, vocab_size: int, device: torch.device) -> None:
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"""
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Joiner initialization.
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Parameters
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----------
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joiner_dim : int
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Input joiner dimension.
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vocab_size : int
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Output joiner dimension, the vocabulary size, the number of BPEs of the model.
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device : torch.device
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The device used to store the layer weights. Should be
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either torch.device("cpu") or torch.device("cuda").
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"""
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super().__init__()
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self.output_linear = torch.nn.Linear(joiner_dim, vocab_size, device=device)
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def forward(self, encoder_out: torch.Tensor, decoder_out: torch.Tensor) -> torch.Tensor:
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"""
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Does a forward pass of the Joiner module. Returns an output tensor after a simple joining.
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Parameters
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----------
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encoder_out : torch.Tensor[torch.float32]
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An output tensor from the encoder after projection of shape (N, joiner_dim).
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decoder_out : torch.Tensor[torch.float32]
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An output tensor from the decoder after projection of shape (N, joiner_dim).
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Returns
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-------
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torch.Tensor[torch.float32]
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A float output tensor of log token probabilities of shape (N, vocab_size).
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"""
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return self.output_linear(torch.tanh(encoder_out + decoder_out))
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